Fault localization method and device

ABSTRACT

A fault localization method includes: obtaining user experience data, network topology data, and resource management data that are of a video service; where the network topology data is used to represent a connection relationship between network devices, and the resource management data is used to represent a connection relationship between user equipment and the network devices; determining a QoE experience indicator of a network device based on the user experience data, the network topology data, and the resource management data; and when QoE represented by the QoE experience indicator of the network device is lower than QoE represented by a device screening threshold, determining the network device as a possible questionable device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of Int'l Patent App. No. PCT/CN2018/085314 filedon May 2, 2018, which claims priority to Chinese Patent App. No.201710643337.8 filed on Jul. 31, 2017, which are incorporated byreference.

TECHNICAL FIELD

The present disclosure relates to the computer field, and in particular,to a fault localization method and device.

BACKGROUND

With rapid development of the network video industry, for example,emergence of Internet Protocol television (IPTV) services andover-the-top (OTT) services, operators have gradually shifted theirfocus from network coverage and network quality assurance to“user-centric” operations, especially focus on user experience. Qualityof user experience is directly related to user market share, andimproving the video user experience can further promote service growth.In an IPTV system, if a network device or link is faulty, for example, aport, subcard, or board of the device is faulty, IPTV users will bedirectly affected, and user experience is greatly affected. Therefore,when the network device is faulty and the user experience deteriorates,the faulty device needs to be accurately located in time, and the faultis rectified in time to ensure good user experience.

At present, in the IPTV field, an emergency manner in which a usercomplains a trouble ticket to reflect an experience problem, to triggera maintenance department to manually troubleshoot a fault is usuallyused. However, manual fault localization is excessively delayed, theuser is affected for a long time, and operations are complicated.

For this disadvantage, a faulty device can be located by monitoring anetwork quality of service (QoS) indicator (for example, a packet lossrate or a delay) and giving an alarm if each QoS indicator exceeds analarm threshold. However, because a QoS exception may not causedeterioration of final user experience, and a device with the QoSexception may not be the faulty device that causes the exception,accuracy of fault localization is low.

SUMMARY

Embodiments of the present disclosure provide a fault localizationmethod and device, and accuracy of fault localization is high.

According to a first aspect, a fault localization method is provided.The method includes: obtaining user experience data, network topologydata, and resource management data that are of a video service, wherethe network topology data is used to represent a connection relationshipbetween network devices, and the resource management data is used torepresent a connection relationship between user equipment and thenetwork devices; determining a quality of experience (QoE) experienceindicator of a network device based on the user experience data, thenetwork topology data, and the resource management data, where the QoEexperience indicator of the network device are determined based on userexperience data of user equipment served by the network device; and whenQoE represented by the QoE experience indicator of the network device islower than QoE represented by a device screening threshold, determiningthe network device as a possible questionable device.

In this embodiment of the present disclosure, the user experience data,the network topology data, and the resource management data that are ofthe video service are obtained, so that the QoE experience indicator ofthe network device can be determined. The QoE experience indicator ofthe network device is determined based on the user experience data ofthe user equipment served by the network device. This is different froma manner in which a QoS indicator of the network device is directlydetermined by obtaining a parameter of the network device. Therefore,compared with a method for performing fault localization by monitoringthe QoS indicator, this method can better reflect user experience andhas higher accuracy.

In a possible implementation, the user experience data includes at leastone of the following items: a video mean opinion score (vMOS), stallingduration, a stalling proportion, stalling frequency, an artifactduration proportion, artifact times, an artifact area proportion, videoquality switch times, and a poor quality proportion of video quality.According to this implementation, the QoE experience indicator of thenetwork device can be determined with reference to one or more of theforegoing items.

In a possible implementation, the network topology data includes atopology connection relationship or a service path of an existingnetwork, and the service path is used to represent a connectionrelationship between the network devices through which service trafficflows. According to this implementation, the user equipment served bythe network device may be determined based on the topology connectionrelationship or the service path of the existing network, so that theQoE experience indicator of the network device may be determined basedon the obtained user experience data.

In a possible implementation, a distribution characteristic of the QoEexperience indicators of a plurality of same hierarchy network devicesincluding the possible questionable device are analyzed, and a networkdevice whose QoE experience indicator is an outlier and that is in theplurality of same hierarchy network devices is determined as aquestionable device when it is determined, based on the distributioncharacteristic, that the QoE experience indicators of the plurality ofsame hierarchy network devices are in skewed distribution. According tothis implementation, statistics and analysis are performed on thedistribution characteristic of the QoE experience indicators of theplurality of same hierarchy network devices including the possiblequestionable device, to determine the questionable device, so thataccuracy of fault localization can be further improved.

In a possible implementation, a first distribution characteristic valueof the QoE experience indicators of the plurality of same hierarchynetwork devices including the possible questionable device isdetermined, where the first distribution characteristic value is used torepresent whether the QoE experience indicators of the plurality of samehierarchy network devices are in skewed distribution; when the firstdistribution characteristic value is greater than a first equilibriumskew threshold, it is determined that the QoE experience indicators ofthe plurality of same hierarchy network devices are in skeweddistribution; and the network device whose QoE experience indicator isthe outlier and that is in the plurality of same hierarchy networkdevices is determined as the questionable device. According to thisimplementation, by using a value relationship between the firstdistribution characteristic value and the first equilibrium skewthreshold, it is determined that the QoE experience indicators of theplurality of same hierarchy network devices are in skewed distribution,so as to determine the questionable device. This manner has highaccuracy. Optionally, the first distribution characteristic value is acoefficient of variation.

In a possible implementation, a first overall characteristic value ofthe QoE experience indicators of the plurality of same hierarchy networkdevices including the possible questionable device is determined, wherethe first overall characteristic value is used to represent an averagelevel of the QoE experience indicators of the plurality of samehierarchy network devices; and the distribution characteristic of theQoE experience indicators of the plurality of same hierarchy networkdevices including the possible questionable device are analyzed, and anetwork device whose QoE experience indicator is greater than the firstoverall characteristic value and that is in the plurality of samehierarchy network devices is determined as the questionable device whenit is determined, based on the distribution characteristic, that the QoEexperience indicators of the plurality of same hierarchy network devicesare in skewed distribution. According to this implementation, the firstoverall characteristic value is determined and the QoE experienceindicators of the plurality of same hierarchy network devices arecompared with the first overall characteristic value, so that a networkdevice whose QoE experience indicator is an outlier and that is in theplurality of same hierarchy network devices is determined, and thenetwork device is determined as the questionable device. This manner hashigh accuracy. Optionally, the first overall characteristic value is anaverage value or a median or an empirically set value used to representan average level. The foregoing average value may be a direct averagevalue or a weighted average value. For example, when a quantity of usersserved by the network device is large, a weighting coefficient of theQoE experience indicator of the network device is large.

Optionally, after the questionable device is initially determined basedon the foregoing manner, the questionable device may further bedetermined in the following manner: A lower confidence limit of aquantity of online users of the plurality of same hierarchy networkdevices is determined; and the questionable device is excluded when aquantity of online users of the questionable device is less than thelower confidence limit. In other words, a network device that hasexcessively few online users and that is in the initially determinedquestionable device is not considered as a questionable device. Thismanner can further improve accuracy of fault localization.

In a possible implementation, when it is determined that the QoEexperience indicators of the plurality of same hierarchy network devicesare not in skewed distribution and that the first overall characteristicvalue is greater than a first empirical threshold, it is determined thatthere is a possible questionable device in at least one upstream networkdevice of the plurality of same hierarchy network devices. According tothis implementation, a possible questionable device in network devicesin a hierarchy in the network may be first analyzed by using the valuerelationship between the QoE experience indicator of the network deviceand the device screening threshold, and then whether there is a possiblequestionable device in the at least one upstream network device isdetermined based on the distribution characteristic of the QoEexperience indicators of the network device in this hierarchy. Thismanner has a relatively low amount of operation and can save processingresources.

In a possible implementation, a second overall characteristic value ofthe QoE experience indicators of a plurality of same hierarchylower-hierarchy network devices of the questionable device isdetermined, where the second overall characteristic value is used torepresent an average level of the QoE experience indicators of theplurality of same hierarchy lower-hierarchy network devices; and thequestionable device is not excluded when it is determined that the QoEexperience indicators of the plurality of same hierarchy lower-hierarchynetwork devices of the questionable device are not in skeweddistribution and that the second overall characteristic value is greaterthan a second empirical threshold. According to this implementation,after the questionable device is initially determined, the distributioncharacteristic of the QoE experience indicators of the plurality of samehierarchy lower-hierarchy network devices of the questionable device canfurther be analyzed, to further determine the questionable device orexclude the questionable device. This manner can improve accuracy ofdetermining the questionable device. Optionally, the second overallcharacteristic value is an average value or a median or an empiricallyset value used to represent an average level. The foregoing averagevalue may be a direct average value or a weighted average value. Forexample, when a quantity of users served by the network device is large,a weighting coefficient of the QoE experience indicator of the networkdevice is large.

In a possible implementation, the questionable device includes aplurality of device internal units in at least one hierarchy, thedistribution characteristic of the QoE experience indicators of aplurality of same hierarchy device internal units of the questionabledevice are analyzed, and a device internal unit whose QoE experienceindicator is an outlier and that is in the plurality of same hierarchydevice internal units is determined as a questionable unit when it isdetermined, based on the distribution characteristic, that the QoEexperience indicators of the plurality of same hierarchy device internalunits are in skewed distribution. According to this implementation,after the questionable device is determined, the distributioncharacteristic of the QoE experience indicators of the plurality of samehierarchy device internal units of the questionable device can befurther analyzed, to determine the questionable unit. This manner canfurther improve accuracy of fault localization.

In a possible implementation, a third overall characteristic value ofthe QoE experience indicators of the plurality of same hierarchy deviceinternal units of the questionable device is determined, where the thirdoverall characteristic value is used to represent an average level ofthe QoE experience indicators of the plurality of same hierarchy deviceinternal units; and when it is determined that the QoE experienceindicators of the plurality of same hierarchy device internal units arenot in skewed distribution and that the third overall characteristicvalue is greater than a third empirical threshold, it is determined thatthere is a questionable unit in at least one upper-hierarchy deviceinternal unit of the plurality of same hierarchy device internal units.According to this implementation, after the questionable device isdetermined, whether there is a questionable unit in the same hierarchyupper-hierarchy device internal units can be determined based on thedistribution characteristic of the QoE experience indicators of theplurality of same hierarchy device internal units of the questionabledevice. In this manner, it is not necessary to analyze the distributioncharacteristic of the QoE experience indicators of the device internalunits in each hierarchy. Therefore, this manner has a relatively lowamount of operation and can save processing resources. Optionally, thethird overall characteristic value is an average value or a median or anempirically set value used to represent an average level. The foregoingaverage value may be a direct average value or a weighted average value.For example, when a quantity of users served by the device internal unitis large, a weighting coefficient of the QoE experience indicator of thedevice internal unit is large.

Optionally, a lower confidence limit of a quantity of online users ofthe plurality of same hierarchy device internal units is determined; andwhen the quantity of online users of the questionable unit is less thanthe lower confidence limit, the questionable unit is excluded. In otherwords, a device internal unit that has excessively few online users andthat is in the initially determined questionable unit is not consideredas a questionable unit. This manner can further improve accuracy offault localization.

In a possible implementation, a fourth overall characteristic value ofthe QoE experience indicators of a plurality of same hierarchylower-hierarchy device internal units of the questionable unit isdetermined; and the questionable unit is not excluded when it isdetermined that the QoE experience indicators of the plurality of samehierarchy lower-hierarchy device internal units are not in skeweddistribution and that the fourth overall characteristic value is greaterthan a fourth empirical threshold. According to this implementation, thequestionable unit is further determined by analyzing the distributioncharacteristic of the QoE experience indicators of the plurality of samehierarchy lower-hierarchy device internal units of the questionableunit, thereby helping improve accuracy of fault localization.Optionally, the fourth overall characteristic value is an average valueor a median or an empirically set value used to represent an averagelevel. The foregoing average value may be a direct average value or aweighted average value. For example, when a quantity of users served bythe device internal unit is large, a weighting coefficient of the QoEexperience indicator of the device internal unit is large.

Optionally, a second distribution characteristic value of the QoEexperience indicators of the plurality of same hierarchy device internalunits is determined, where the second distribution characteristic valueis used to represent whether the QoE experience indicators of theplurality of same hierarchy device internal units are in skeweddistribution; when the second distribution characteristic value isgreater than a second equilibrium skew threshold, it is determined thatthe QoE experience indicators of the plurality of same hierarchy deviceinternal units are in skewed distribution; and a device internal unitwhose QoE experience indicator is an outlier and that is in theplurality of same hierarchy device internal units is determined as thequestionable unit. According to this implementation, by using a valuerelationship between the second distribution characteristic value andthe second equilibrium skew threshold, it is determined that the QoEexperience indicators of the plurality of same hierarchy device internalunits are in skewed distribution, so as to determine the questionableunit. This manner has high accuracy. Optionally, the second distributioncharacteristic value is a coefficient of variation.

Optionally, a fifth overall characteristic value of the QoE experienceindicators of the plurality of same hierarchy device internal units isdetermined, where the fifth overall characteristic value is used torepresent an average level of the QoE experience indicators of theplurality of same hierarchy device internal units; and a distributioncharacteristic of the QoE experience indicators of the plurality of samehierarchy device internal units are analyzed, and a device internal unitwhose QoE experience indicator is greater than the fifth overallcharacteristic value and that is in the plurality of same hierarchydevice internal units is determined as a questionable unit when it isdetermined, based on the distribution characteristic, that the QoEexperience indicators of the plurality of same hierarchy device internalunits are in skewed distribution. According to this implementation, thefifth overall characteristic value is determined and the QoE experienceindicators of the plurality of same hierarchy device internal units arecompared with the fifth overall characteristic value, so that a deviceinternal unit whose QoE experience indicator is the outlier and that isin the plurality of same hierarchy device internal units is determined,and the device internal unit is determined as a questionable unit. Thismanner has high accuracy. Optionally, the fifth overall characteristicvalue is an average value or a median or an empirically set value usedto represent an average level. The foregoing average value may be adirect average value or a weighted average value. For example, when aquantity of users served by the device internal unit is large, aweighting coefficient of the QoE experience indicator of the deviceinternal unit is large.

In a possible implementation, the QoE experience indicators of aplurality of lower-hierarchy devices of the questionable device areclustered, where each cluster includes at least one QoE experienceindicator; a proportion of a cluster including a largest quantity of QoEexperience indicators in a total quantity of the QoE experienceindicators of the plurality of lower-hierarchy devices is determined asa first similarity aggregation degree of the QoE experience indicatorsof the plurality of lower-hierarchy devices of the questionable device;and when the first similarity aggregation degree is greater than a firstsimilarity aggregation degree threshold, the questionable device isdetermined as a faulty device. According to this implementation, thesimilarity aggregation degree of the QoE experience indicators of theplurality of lower-hierarchy devices of the questionable device isdetermined, to further determine whether the questionable device is afaulty device, so that accuracy of fault localization is furtherimproved.

In a possible implementation, QoE experience indicators of a pluralityof lower-hierarchy units of the questionable unit are clustered, whereeach cluster includes at least one QoE experience indicator; aproportion of a cluster including a largest quantity of QoE experienceindicators in a total quantity of the QoE experience indicators of theplurality of lower-hierarchy units is determined as a second similarityaggregation degree of the QoE experience indicators of the plurality oflower-hierarchy units of the questionable unit is determined; and whenthe second similarity aggregation degree is greater than a secondsimilarity aggregation degree threshold, the questionable unit isdetermined as a faulty unit. According to this implementation, thesimilarity aggregation degree of the QoE experience indicators of theplurality of lower-hierarchy units of the questionable unit isdetermined, to further determine whether the questionable unit is afaulty unit, so that accuracy of fault localization is further improved.

In a possible implementation, when there are a plurality of samehierarchy questionable units, a third similarity aggregation degree ofthe QoE experience indicators of the plurality of lower-hierarchy unitsof the plurality of questionable units is determined; and the pluralityof questionable units are all determined as faulty units when the thirdsimilarity aggregation degree is greater than a third similarityaggregation degree threshold, and a proportion of a quantity of QoEexperience indicators of each lower-hierarchy unit in a clustercorresponding to the third similarity aggregation degree in a totalquantity of the QoE experience indicators of a questionable unit towhich the lower-hierarchy unit belongs is greater than a presetproportion. According to this implementation, a joint analysis isperformed on the lower-hierarchy units of the plurality of samehierarchy questionable units, so that missing of faulty units can beavoided. Problems such as cross-board can be found in this manner, andaccuracy of fault localization is high.

In a possible implementation, the QoE experience indicator is a poor-QoErate; an indicator algorithm corresponding to the poor-QoE rate of eachnetwork device is: the poor-QoE rate of the network device is equal to atotal quantity of poor-QoE users of the network device divided by atotal quantity of users of the network device; and/or an indicatoralgorithm corresponding to the poor-QoE rate of each device internalunit is: the poor-QoE rate of the device internal unit is equal to atotal quantity of poor-QoE users of the device internal unit divided bya total quantity of users of the device internal unit, where whether auser corresponding to the user experience data is a poor-QoE user isdetermined based on a value relationship between the user experiencedata and an experience threshold; and that the QoE represented by theQoE experience indicator of the network device is lower than the QoErepresented by the device screening threshold specifically includes thatthe poor-QoE rate of the network device is less than the devicescreening threshold. Optionally, when the user experience data includesonly one of the foregoing items, for example, includes only the vMOS,there may be only one experience threshold, and the experience thresholdis an experience threshold corresponding to the vMOS; and when the userexperience data includes some or all of the foregoing items, forexample, includes the vMOS and the stalling duration, each item may havean experience threshold, and whether a user corresponding to the userexperience data is a poor-QoE user is comprehensively determined basedon a value relationship between the user experience data of each itemand the corresponding experience threshold. According to thisimplementation, the manner of determining the QoE experience indicatoris simple and easy to implement, and an experience status of usersserved by the network devices can be accurately reflected.

Optionally, the QoE experience indicator is a VMOS average value or anaverage value of the stalling proportion. When the QoE experienceindicator is the VMOS average value, the smaller VMOS average valueindicates poorer user experience.

According to yet another aspect, an embodiment of the present disclosureprovides a fault localization device. The device can implement functionsperformed in the foregoing method design of the first aspect. Thefunctions may be implemented by hardware, or may be implemented byhardware executing corresponding software. The hardware or the softwareincludes one or more modules corresponding to the functions.

In a possible design, a structure of the device includes a processor,and the processor is configured to support the device in performing acorresponding function in the foregoing method in the first aspect. Thedevice may further include a memory. The memory is configured to coupleto the processor, and store a program instruction and data for thedevice. The device may further include a communications interface, andthe communications interface is configured to: obtain user experiencedata or send alarm information, or the like.

According to another aspect, an embodiment of the present disclosureprovides a chip. The chip may be disposed in a device, and the chipincludes a processor and an interface. The processor is configured tosupport the chip in performing a corresponding function in the methodaccording to the first aspect. The interface is configured to supportcommunication between the chip and another chip or another networkelement. The chip may further include a memory. The memory is configuredto couple to the processor, and store a program instruction and data forthe chip.

According to yet another aspect, an embodiment of the present disclosureprovides a computer storage medium configured to store a computersoftware instruction used by the foregoing device. The computer storagemedium includes a program designed to perform the foregoing firstaspect.

According to yet another aspect, an embodiment of the present disclosureprovides a computer program product that includes an instruction, andwhen the program is executed by a computer, the instruction enables thecomputer to perform a function performed by the device in the foregoingmethod design.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic architectural diagram of a system according to anembodiment of the present disclosure;

FIG. 2 is a flowchart of a fault localization method according to anembodiment of the present disclosure;

FIG. 3A is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 3B is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 3C is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 3D is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 4A is a flowchart of a method of step 204 in any one of FIG. 3A toFIG. 3D;

FIG. 4B is a flowchart of another method of step 204 in any one of FIG.3A to FIG. 3D;

FIG. 4C is a flowchart of another method of step 204 in any one of FIG.3A to FIG. 3D;

FIG. 5A is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 5B is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 5C is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 5D is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 6A is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 6B is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 6C is a flowchart of another fault localization method according toan embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a device-board-port connectionrelationship;

FIG. 8 is a schematic diagram of calculating an OLT poor-QoE rateaccording to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of sifting a possible questionable OLT byusing a poor-QoE rate threshold according to an embodiment of thepresent disclosure;

FIG. 10A is a schematic diagram showing distribution of a poor-QoE rateof each PON board of a possible questionable OLT 4 in FIG. 9;

FIG. 10B is a schematic diagram showing distribution of quantities ofonline users of the PON boards of the possible questionable OLT 4 inFIG. 9;

FIG. 11A is a schematic diagram of a connection relationship when a PONboard 2 in FIG. 10A is a possible skewed PON board;

FIG. 11B is a diagram showing distribution of a poor-QoE rate of eachPON port of the PON board 2;

FIG. 11C is a schematic diagram of a poor quality behavior correlationmatrix of each PON port of the PON board 2;

FIG. 12 is a schematic structural diagram of a fault localization deviceaccording to an embodiment of the present disclosure;

FIG. 13 is a schematic diagram of interaction of each module included inFIG. 12 according to an embodiment of the present disclosure;

FIG. 14 is a schematic structural diagram of another fault localizationdevice according to an embodiment of the present disclosure; and

FIG. 15 is a schematic structural diagram of still another faultlocalization device according to an embodiment of the presentdisclosure.

DESCRIPTION OF EMBODIMENTS

FIG. 1 is a schematic architectural diagram of a system according to anembodiment of the present disclosure. The system includes a televisionset 101, a set-top box (STB) 102, an optical network terminal (ONT) 103,an optical line terminal (OLT) 104, a limit switch (LSW) 105, abroadband remote access server (BRAS) 106, a core router (CR) 107, aprovincial backbone network 108, an IPTV server 109, a user experiencecollection system 1010, a topology and resource management collectionsystem 1011, and a fault localization analysis system 1012. The userexperience collection system 1010, connected to a related device (forexample, the STB), is configured to collect user experience data, wherethe user experience data is used to represent user experience. Thetopology and resource management collection system 1011, connected torelated devices (for example, the OLT, the LSW, the BRAS, and the CR),is configured to obtain network topology data and resource managementdata, where the network topology data is used to represent a connectionrelationship between network devices, and the resource management datais used to represent a connection relationship between user equipmentand the network devices. In one example, the user experience data iscollected by using a probe and reported to the user experiencecollection system 1010; the topology and resource management collectionsystem 1011 collects the network topology data (for example, a topologyconnection relationship or a service path of an existing network, wherethe service path is used to represent a connection relationship betweenthe network devices through which service traffic flows) and theresource management data; then the user experience collection system1010 and the topology and resource management collection system 1011report the user experience data, the network topology data, and theresource management data to the fault localization analysis system 1012,and the fault localization analysis system 1012 detects a faulty deviceor further detects a faulty unit in a faulty device after detecting thefaulty device. The probe configured to collect the user experience datamay be deployed in, but are not limited to, the following two positions.A position 1 is on the set top box, that is, the probe is deployed onthe set top box, and a position 2 is between the BRAS and the CR, thatis, the probe is deployed between the BRAS and the CR. The faultlocalization analysis system 1012 in this embodiment of the presentdisclosure, the user experience collection system 1010, and the topologyand resource management collection system 1011 may be separatelydeployed in different devices, or may be integrated in a same device.

In the foregoing system architecture, in a schematic illustration, onenetwork device of each type is drawn. It may be understood that anactual system may include all the network devices in FIG. 1, or mayinclude only some of the network devices in FIG. 1, and a quantity ofnetwork devices of each type may be one or more.

According to a fault localization method provided in embodiments of thepresent disclosure, user equipment served by a network device may bedetermined based on the network topology data and the resourcemanagement data, so that a QoE experience indicator of the networkdevice is determined based on user experience data of the user equipmentserved by the network device. When QoE represented by the QoE experienceindicator of the network device is lower than QoE represented by apreset level device screening threshold, the network device isdetermined as a possible questionable device.

In one example, a QoE experience indicator of each network device in thesystem may be determined first, and then a possible questionable deviceis sifted out based on the QoE experience indicator of each networkdevice. This manner is highly accurate, and can avoid missing thepossible questionable device.

In another example, QoE experience indicators of network devices in ahierarchy in the system may be determined first, and then a possiblequestionable device in the hierarchy is sifted out based on the QoEexperience indicator of each network device in the hierarchy. Thismanner considers both accuracy and efficiency, and can save processingresources.

If a network device A is an upstream network device of a network deviceB, and the two network devices are adjacent to each other, the networkdevice A is referred to as an upper-hierarchy network device of thenetwork device B, and the network device B is referred to as alower-hierarchy network device of the network device A. It may beunderstood that a plurality of network devices with a commonupper-hierarchy network device are referred to as same hierarchy networkdevices; similarly, when a network device includes device internal unitsin a plurality of hierarchies, a plurality of device internal units witha common upper-hierarchy device internal unit are referred to as samehierarchy device internal units. In general, nodes with a commonupper-hierarchy node are referred to as same hierarchy nodes.

To further improve accuracy of fault localization, in one example, basedon sifting out a possible questionable device by using a valuerelationship between a QoE experience indicator of a network device anda device screening threshold, a distribution characteristic of QoEexperience indicators of a plurality of same hierarchy network devicesincluding the possible questionable device are further analyzed todetermine a questionable device, and then whether the questionabledevice is a faulty device is determined based on a similarityaggregation degree of the QoE experience indicators of lower-hierarchydevices of the questionable device. In the fault localization method,not only the QoE experience indicator of one network device is takeninto account, but also the QoE experience indicators of other networkdevices in the network are referenced, so that the faulty device isdetermined based on a comprehensive analysis, and the method has highaccuracy.

Optionally, in the embodiments of the present disclosure, a process ofdetermining the questionable device may be divided into the followingthree phases: phase 1: A distribution characteristic of QoE experienceindicators of a plurality of same hierarchy network devices including apossible questionable device are analyzed, to initially determine aquestionable device; phase 2: The distribution characteristic of the QoEexperience indicators of a plurality of same hierarchy lower-hierarchynetwork devices of the questionable device are analyzed, to furtherdetermine the questionable device; phase 3: The questionable device isdetermined again based on a quantity of online users of the questionabledevice.

It may be understood that the phase 1 may be separately combined withthe phase 2 or the phase 3, to relatively accurately identify thequestionable device. For example, the phase 1 is performed first, andthen the phase 2 is performed; or the phase 1 is performed first, andthen the phase 3 is performed.

In one example, after the questionable device is determined, QoEexperience indicators of device internal units of the questionabledevice may be further analyzed to determine a questionable unit; andwhether the questionable unit is a faulty unit is determined based on asimilarity aggregation degree of QoE experience indicators oflower-hierarchy units of the questionable unit.

In the embodiments of the present disclosure, the fault localizationmethod has a plurality of implementations. For ease of understanding,the following describes the method processes.

FIG. 2 is a flowchart of a fault localization method according to anembodiment of the present disclosure. The method may be based on thesystem architecture shown in FIG. 1, and an execution body may be thefault localization analysis system in FIG. 1. The method includes thefollowing steps.

Step 201: Obtain user experience data, network topology data, andresource management data that are of a video service.

The user experience data is used to represent user experience, thenetwork topology data is used to represent a connection relationshipbetween network devices, and the resource management data is used torepresent a connection relationship between user equipment and thenetwork devices.

In one example, the user experience data includes at least one of thefollowing items: a vMOS, stalling duration, a stalling proportion,stalling frequency, an artifact duration proportion, artifact times, anartifact area proportion, video quality switch times, and a poor qualityproportion of video quality. Video quality switch means that a userswitches video quality, for example, switches the video quality fromstandard definition to high definition.

In one example, the network topology data includes a topology connectionrelationship or a service path of an existing network, and the servicepath is used to represent a connection relationship between the networkdevices through which service traffic flows.

Step 202: Determine a QoE experience indicator of a network device basedon the user experience data, the network topology data, and the resourcemanagement data.

The QoE experience indicator of the network device is determined basedon user experience data of user equipment served by the network device.

In one example, the user equipment served by the network device may befirst determined based on the network topology data and the resourcemanagement data, and then the QoE experience indicator of the networkdevice may be determined based on the user experience data of the userequipment served by the network device.

It may be understood that a QoE experience indicator of each networkdevice in the system may be determined, or only a QoE experienceindicator of each network device in a same hierarchy in the system maybe determined. In this embodiment of the present disclosure, an examplein which the QoE experience indicator of each network device in a samehierarchy in the system is first determined is used for description, andcertainly, that the QoE experience indicator of each network device inanother hierarchy is determined later as required is not excluded.

The QoE experience indicator and an indicator algorithm corresponding tothe QoE experience indicator may be configured first, and then the QoEexperience indicator in each hierarchy is determined based on the QoEexperience indicator and the indicator algorithm corresponding to theQoE experience indicator.

In one example, the QoE experience indicator is a poor-QoE rate.

The indicator algorithm corresponding to the poor-QoE rate of eachnetwork device is: the poor-QoE rate of the network device is equal to atotal quantity of poor-QoE users of the network device divided by atotal quantity of users of the network device. For example, if the totalquantity of users of the network device is 10, and the total quantity ofpoor-QoE users of the network device is 2, the network device has a 20%poor-QoE rate.

Whether a user corresponding to the user experience data is a poor-QoEuser may be determined based on a value relationship between the userexperience data and an experience threshold.

For example, when the user experience data of a user includes one of theforegoing items, if user experience represented by a value of the itemis less than an experience level represented by an experience thresholdcorresponding to the item, the user is determined as a poor-QoE user;and if the user experience represented by the value of the item isgreater than or equal to the experience level represented by theexperience threshold corresponding to the item, the user is determinedas a non-poor-QoE user. Example 1: The user experience data of the userincludes the vMOS, and if a value of the vMOS is less than an experiencethreshold corresponding to the vMOS, the user is determined as apoor-QoE user. Example 2: The user experience data of the user includesthe stalling duration, and if a value of the stalling duration isgreater than an experience threshold corresponding to the stallingduration, the user is determined as a poor-QoE user.

For another example, when the user experience data of a user includessome or all of the foregoing items, if user experience represented by avalue of each item is less than an experience level represented by anexperience threshold corresponding to the item, the user is determinedas a poor-QoE user; and if the user experience represented by the valueof at least one item is greater than or equal to the experience levelrepresented by the experience threshold corresponding to the item, theuser is determined as a non-poor-QoE user.

For another example, when the user experience data of a user includessome or all of the foregoing items, values of the items may be weightedand summed, and if a summed value is less than an experience threshold,the user is determined as a poor-QoE user; and if the summed value isgreater than or equal to the experience threshold, the user isdetermined as a non-poor-QoE user.

Optionally, the QoE experience indicator may alternatively be an averagevalue of one item of the user experience data, such as an average valueof the VMOS and an average value of the stalling proportion. If the QoEexperience indicator is the average value of the VMOS, a smaller QoEexperience indicator indicates poorer QoE.

Step 203: When QoE represented by the QoE experience indicator of thenetwork device is lower than QoE represented by a device screeningthreshold, determine the network device as a possible questionabledevice.

The device screening threshold may be preset, or may be determined basedon a distribution status of QoE experience indicators of a plurality ofnetwork devices (for example, a plurality of OLTs) of a same type as thenetwork device.

If a larger QoE experience indicator indicates lower QoE, for example,the QoE experience indicator is an average value of poor-QoE rates or anaverage value of the stalling proportions, a specific implementation ofstep 203 may be that when the QoE experience indicator of the networkdevice is greater than the device screening threshold, the networkdevice is determined as the possible questionable device.

If a larger QoE experience indicator indicates higher QoE, for example,the QoE experience indicator is an average value of the VMOSs, aspecific implementation of step 203 may be that when the QoE experienceindicator of the network device is less than the device screeningthreshold, the network device is determined as the possible questionabledevice.

In this embodiment of the present disclosure, the user experience data,the network topology data, and the resource management data that are ofthe video service may be obtained, so that the QoE experience indicatorof the network device can be determined. The QoE experience indicator ofthe network device is determined based on the user experience data ofthe user equipment served by the network device. This is different froma manner in which a QoS indicator of the network device is directlydetermined by obtaining a parameter of the network device. Therefore,compared with a method for performing fault localization by monitoringthe QoS indicator, this method can better reflect user experience andhas higher accuracy.

In the embodiment shown in FIG. 2, after the network device isdetermined as the possible questionable device, the process may end, andthe possible questionable device can be manually checked later. Tofurther improve accuracy of fault localization, in another embodiment ofthe present disclosure, a questionable device is further determinedbased on the determined possible questionable device, and thequestionable device is more likely to be faulty than the possiblequestionable device.

FIG. 3A is a flowchart of another fault localization method according toan embodiment of the present disclosure. Based on the method processshown in FIG. 2, a questionable device is further located throughequilibrium skew analysis. In addition to the foregoing steps 201 to203, the method further includes the following step.

Step 204: Analyze a distribution characteristic of the QoE experienceindicators of a plurality of same hierarchy network devices includingthe possible questionable device, and determine a network device whoseQoE experience indicator is an outlier and that is in the plurality ofsame hierarchy network devices as a questionable device when it isdetermined, based on the distribution characteristic, that the QoEexperience indicators of the plurality of same hierarchy network devicesare in skewed distribution.

It may be understood that when there are a plurality of possiblequestionable devices in the system that belong to a same hierarchy, step204 may be performed on only one of the plurality of possiblequestionable devices that belong to the same hierarchy; and when thereare a plurality of possible questionable devices in the system thatbelong to different hierarchies, step 204 may be separately performed onthe plurality of possible questionable devices that belong to differenthierarchies. For example, if four possible questionable devices in asame hierarchy in the system are respectively a possible questionabledevice A, a possible questionable device B, a possible questionabledevice C, and a possible questionable device D, step 204 may beperformed only for the possible questionable device A. There is agreater likelihood that at least one of the plurality of possiblequestionable devices is determined as the questionable device.

As shown in FIG. 4A, in one example, step 204 includes the followingsteps.

Step 2041: Determine a first distribution characteristic value of theQoE experience indicators of the plurality of same hierarchy networkdevices including the possible questionable device, where the firstdistribution characteristic value is used to represent whether the QoEexperience indicators of the plurality of same hierarchy network devicesare in skewed distribution.

Same hierarchy network devices may be understood as network devices witha same upper-hierarchy network device, and network devices with a sameupper-hierarchy network device are usually network devices of a sametype, for example, are all OLTs.

In one example, the first distribution characteristic value is acoefficient of variation.

Step 2042: When the first distribution characteristic value is greaterthan a first equilibrium skew threshold, determine that the QoEexperience indicators of the plurality of same hierarchy network devicesare in skewed distribution.

Step 2043: Determine the network device whose QoE experience indicatoris the outlier and that is in the plurality of same hierarchy networkdevices as the questionable device.

According to this implementation, that the QoE experience indicators ofthe plurality of same hierarchy network devices are in skeweddistribution is determined by using a value relationship between thefirst distribution characteristic value and the first equilibrium skewthreshold, to determine the questionable device. This manner has highaccuracy.

As shown in FIG. 4B, in another example, step 204 includes the followingsteps.

Step 2044: Determine a first overall characteristic value of the QoEexperience indicators of the plurality of same hierarchy network devicesincluding the possible questionable device, where the first overallcharacteristic value is used to represent an average level of the QoEexperience indicators of the plurality of same hierarchy networkdevices.

Optionally, the first overall characteristic value is an average valueor a median or an empirically set value used to represent the averagelevel. The foregoing average value may be a direct average value or aweighted average value. For example, when a quantity of users served bythe network device is large, a weighting coefficient of the QoEexperience indicator of the network device is large.

Step 2045: Analyze the distribution characteristic of the QoE experienceindicators of the plurality of same hierarchy network devices includingthe possible questionable device, and determine the network device whoseQoE experience indicator is greater than the first overallcharacteristic value and that is in the plurality of same hierarchynetwork devices as the questionable device when it is determined, basedon the distribution characteristic, that the QoE experience indicatorsof the plurality of same hierarchy network devices are in skeweddistribution.

According to this implementation, the first overall characteristic valueis determined and the QoE experience indicators of the plurality of samehierarchy network devices are compared with the first overallcharacteristic value, so that the network device whose QoE experienceindicator is the outlier and that is in the plurality of same hierarchynetwork devices is determined, and the network device is determined asthe questionable device. This manner has high accuracy.

As shown in FIG. 4C, in another example, based on the method processshown in FIG. 4B, in addition to step 2044 and step 2045, step 204further includes the following step.

Step 2046: When it is determined that the QoE experience indicators ofthe plurality of same hierarchy network devices are not in skeweddistribution and that the first overall characteristic value is greaterthan a first empirical threshold, determine that there is a possiblequestionable device in at least one upstream network device of theplurality of same hierarchy network devices.

According to this implementation, when the QoE experience indicators ofthe plurality of same hierarchy network devices are evenly distributedand the first overall characteristic value is abnormal, it is determinedthat there is a possible questionable device in the at least upstreamnetwork device of the plurality of same hierarchy network devices. Eachmethod included in the embodiment in FIG. 3A may be performed on thepossible questionable device in the at least one upstream networkdevice, to further locate the questionable device through equilibriumskew analysis. Details are not described herein again.

It may be understood that the embodiments corresponding to FIG. 4A, FIG.4B, and FIG. 4C may be combined with each other to form a newembodiment. For example, in an embodiment, whether the QoE experienceindicators of the plurality of same hierarchy network devices are inskewed distribution is first determined by using the value relationshipbetween the first distribution characteristic value and the firstequilibrium skew threshold. When the QoE experience indicators of theplurality of same hierarchy network devices are in skewed distribution,the first overall characteristic value is determined, and the QoEexperience indicators of the plurality of same hierarchy network devicesare compared with the first overall characteristic value, so that thenetwork device whose QoE experience indicator is the outlier and that isin the plurality of same hierarchy network devices is determined, andthe network device is determined as the questionable device. Inaddition, when the QoE experience indicators of the plurality of samehierarchy network devices are not in skewed distribution and the firstoverall characteristic value is greater than the first empiricalthreshold, it is determined that there is the possible questionabledevice in the at least one upstream network device of the plurality ofsame hierarchy network devices. When it is determined that there is thepossible questionable device in the at least one upstream network deviceof the plurality of same hierarchy network devices, a commonupper-hierarchy network device of the plurality of same hierarchynetwork devices may be used as the possible questionable device, andstep 204 is performed for the possible questionable device.

In one example, as shown in FIG. 3B, after the questionable device isdetermined in step 204, the questionable device may also be furtherconfirmed or excluded based on a quantity of online users of the networkdevice. The method further includes steps 205 and 206.

Step 205: Determine a lower confidence limit of a quantity of onlineusers of the plurality of same hierarchy network devices including thequestionable device.

Step 206: Exclude the questionable device when the quantity of onlineusers of the questionable device is less than the lower confidencelimit.

It may be understood that the questionable device is excluded, that is,the questionable device is confirmed as a normal device.

According to this implementation, the questionable device withexcessively few online users may be reconfirmed as the normal device.

In one example, as shown in FIG. 3C, after the questionable device islocated in step 204, the questionable device may also be furtherconfirmed or excluded with reference to a distribution characteristic ofQoE experience indicators of lower-hierarchy network devices. The methodfurther includes steps 207 and 208.

Step 207: Determine a second overall characteristic value of the QoEexperience indicators of a plurality of same hierarchy lower-hierarchynetwork devices of the questionable device, where the second overallcharacteristic value is used to represent an average level of the QoEexperience indicators of the plurality of same hierarchy lower-hierarchynetwork devices.

Step 208: Skip excluding the questionable device when it is determinedthat the QoE experience indicators of the plurality of same hierarchylower-hierarchy network devices of the questionable device are not inskewed distribution and that the second overall characteristic value isgreater than a second empirical threshold.

Whether the QoE experience indicators of the plurality of same hierarchylower-hierarchy network devices of the questionable device are in skeweddistribution may be determined, but is not limited to, in a manner inwhich the first distribution characteristic value is compared with thefirst equilibrium skew threshold.

It may be understood that, the questionable device is not excluded, thatis, the questionable device is further determined.

In addition, the method may further include: excluding the questionabledevice when it is determined that the QoE experience indicators of theplurality of same hierarchy lower-hierarchy network devices of thequestionable device are in skewed distribution; or excluding thequestionable device when it is determined that the QoE experienceindicators of the plurality of same hierarchy lower-hierarchy networkdevices of the questionable device are not in skewed distribution andthat the second overall characteristic value is less than or equal tothe second empirical threshold. The excluding questionable device is toreconfirm the questionable device as a normal network device. Inaddition, when it is determined that the QoE experience indicators ofthe plurality of same hierarchy lower-hierarchy network devices of thequestionable device are in skewed distribution, a network device whoseQoE experience indicator is an outlier and that is in the plurality ofsame hierarchy lower-hierarchy network devices may further be determinedas the questionable device, and steps 205 and 206 and/or steps 207 and208 are performed for the determined questionable device.

According to this implementation, after the questionable device isinitially determined, the distribution characteristic of the QoEexperience indicators of the plurality of same hierarchy lower-hierarchynetwork devices of the questionable device are analyzed, to furtherdetermine or exclude the questionable device, thereby improving accuracyof determining the questionable device.

It may be understood by a person skilled in the art that the foregoingsteps may be combined to form a plurality of possible embodiments. Forexample, a scheme includes steps 201 to 204, that is, the scheme shownin FIG. 3A; another scheme includes steps 201 to 206, that is, thescheme shown in FIG. 3B; still another scheme includes steps 201 to 204,207, and 208, that is, the scheme shown in FIG. 3C; and yet anotherscheme includes steps 201 to 208, that is, the scheme shown in FIG. 3D.

Usually, inside of a network device includes device internal units in atleast one hierarchy, for example, a board-subcard-port-link hierarchy.

In one example, the questionable device includes a plurality of deviceinternal units in at least one hierarchy. FIG. 5A is a flowchart ofstill another fault localization method according to an embodiment ofthe present disclosure. Based on locating the questionable device, aninternal unit of the questionable device (or questionable unit) isfurther located through equilibrium skewed analysis. The method may bebased on FIG. 3A, FIG. 3B, FIG. 3C, or FIG. 3D. FIG. 5A is only anexample of the method based on FIG. 3A for description. In addition tothe foregoing steps 201 to 204, the method further includes thefollowing step.

Step 209: Analyze the distribution characteristic of the QoE experienceindicators of the plurality of same hierarchy device internal units ofthe questionable device, and determine a device internal unit whose QoEexperience indicator is an outlier and that is in the plurality of samehierarchy device internal units as a questionable unit when it isdetermined, based on the distribution characteristic, that the QoEexperience indicators of the plurality of same hierarchy device internalunits are in skewed distribution.

In one example, the questionable device includes device internal unitsin a plurality of hierarchies, and step 209 may be performed to analyzeonly the QoE experience indicators of device internal units in ahierarchy. For example, the questionable device includes device internalunits in three hierarchies, which are respectively, a first hierarchy, asecond hierarchy, and a third hierarchy from bottom to top, and only theQoE experience indicators of the device internal units in the secondhierarchy may be analyzed. If necessary, the QoE experience indicatorsof the device internal units at the upper hierarchy or lower hierarchyof the second hierarchy are analyzed subsequently. The first hierarchyis referred to as a lower hierarchy of the second hierarchy, and thethird hierarchy is referred to as an upper hierarchy of the secondhierarchy.

A method for determining whether the QoE experience indicators of theplurality of same hierarchy device internal units are in skeweddistribution based on the distribution characteristic may be similar tothe method for determining whether the QoE experience indicators of theplurality of same hierarchy network devices are in skewed distributionbased on the distribution characteristic. Details are not describedherein again.

Similarly, a method for searching for a device internal unit whose QoEexperience indicator is an outlier and that is on the plurality of samehierarchy device internal units may be similar to the method forsearching for the network device whose QoE experience indicator anoutlier and that in the plurality of same hierarchy network devices.Details are not described herein again.

Optionally, as shown in FIG. 5B, the method further includes thefollowing steps.

Step 2010: Determine a third overall characteristic value of the QoEexperience indicators of a plurality of same hierarchy device internalunits of the questionable device, where the third overall characteristicvalue is used to represent an average level of the QoE experienceindicators of the plurality of same hierarchy device internal units.

Step 2011: When it is determined that the QoE experience indicators ofthe plurality of same hierarchy device internal units are not in skeweddistribution and that the third overall characteristic value is greaterthan a third empirical threshold, determine that there is a questionableunit in at least one upper-hierarchy device internal unit of theplurality of same hierarchy device internal units.

It may be understood that after it is determined in step 2011 that thereis the questionable unit in the at least one upper-hierarchy deviceinternal unit of the plurality of same hierarchy device internal units,the QoE experience indicator of the at least one upper-hierarchy deviceinternal unit of the plurality of same hierarchy device internal unitsmay further be analyzed in any manner included in FIG. 5A and FIG. 5B,to determine the questionable unit in the at least one upper-hierarchydevice internal unit. For example, step 209 and/or 2010 to 2011 may beperformed by using a neighboring upper-hierarchy device internal unitcommon to the plurality of same hierarchy device internal units as thequestionable unit.

In one example, after step 209, the questionable unit may further beexcluded or confirmed with reference to a quantity of online users. Asshown in FIG. 5C, the method further includes the following steps.

Step 2012: Determine a lower confidence limit of a quantity of onlineusers of the plurality of same hierarchy device internal units includingthe questionable unit.

Step 2013: Exclude the questionable unit when the quantity of onlineusers of the questionable unit is less than the lower confidence limit.

The excluding questionable unit is to reconfirm the questionable unit asa normal unit.

According to this implementation, a questionable unit with excessivelyfew online users may be excluded to improve accuracy of determining aquestionable unit.

In one example, after step 209, the questionable unit may further beexcluded or confirmed with reference to the distribution characteristicof the QoE experience indicators of the plurality of same hierarchylower-hierarchy device internal units of the questionable unit. As shownin FIG. 5D, the method further includes the following steps.

Step 2014: Determine a fourth overall characteristic value of the QoEexperience indicators of the plurality of same hierarchy lower-hierarchydevice internal units of the questionable unit.

Step 2015: Skip excluding the questionable unit when it is determinedthat the QoE experience indicators of the plurality of same hierarchylower-hierarchy device internal units are not in skewed distribution andthat the fourth overall characteristic value is greater than a fourthempirical threshold.

According to this implementation, after the questionable unit isinitially determined, the questionable unit may further be determined orexcluded based on the distribution characteristic of the QoE experienceindicators of the plurality of same hierarchy lower-hierarchy deviceinternal units of the questionable unit, thereby further improvingaccuracy of determining the questionable unit.

In addition, the method may further include: excluding the questionableunit when it is determined that the QoE experience indicators of theplurality of same hierarchy lower-hierarchy device internal units of thequestionable unit are in skewed distribution; or excluding thequestionable unit when it is determined that the QoE experienceindicators of the plurality of same hierarchy lower-hierarchy deviceinternal units of the questionable unit are not in skewed distributionand that the fourth overall characteristic value is less than or equalto the fourth empirical threshold. The excluding the questionable unitis to reconfirm the questionable unit as a normal device internal unit.In addition, when it is determined that the QoE experience indicators ofthe plurality of same hierarchy lower-hierarchy device internal units ofthe questionable unit are in skewed distribution, a device internal unitwhose QoE experience indicator is an outlier and that is in theplurality of same hierarchy lower-hierarchy device internal units mayfurther be determined as the questionable unit, and steps 2012 and 2013and/or steps 2014 and 2015 are performed for the device internal unitthat is determined as the questionable unit and that is in the pluralityof same hierarchy device internal units.

It may be understood by a person skilled in the art that the foregoingsteps may be combined to form a plurality of possible embodiments. Forexample, one scheme includes steps 201 to 204, and step 209, that is,the scheme shown in FIG. 5A; another scheme includes steps 201 to 204,and steps 209 to 2011, that is, the scheme shown in FIG. 5B; stillanother scheme includes steps 201 to 204, and steps 209, 2012 and 2013,that is, the scheme shown in FIG. 5C; and yet another scheme includessteps 201 to 204, and steps 209, 2014, and 2015, that is, the schemeshown in FIG. 5D.

In one example, after a questionable device is determined, a similarityaggregation degree of QoE experience indicators of a plurality oflower-hierarchy devices of the questionable device may further beanalyzed to further determine whether the questionable device is faulty.As shown in FIG. 6A, in addition to steps 201 to 204, the method furtherincludes the following steps.

Step 2016: Cluster the QoE experience indicators of a plurality oflower-hierarchy devices of the questionable device, where each clusterincludes at least one QoE experience indicator.

For example, the lower-hierarchy devices of an OLT include an ONT 1, anONT 2, an ONT 3, and an ONT 4.

A total quantity of the QoE experience indicators of the lower-hierarchydevices is 4, and after the QoE experience indicator of the ONT 1, theQoE experience indicator of the ONT 2, the QoE experience indicator ofthe ONT 3, and the QoE experience indicator of the ONT 4 are clustered,two clusters of the QoE experience indicators are obtained. A firstcluster includes one QoE experience indicator, which is the QoEexperience indicator of the ONT 1. A second cluster includes three QoEexperience indicators, which are the QoE experience indicator of the ONT2, the QoE experience indicator of the ONT 3, and the QoE experienceindicator of the ONT 4.

Step 2017: Determine that a proportion of a cluster including a largestquantity of QoE experience indicators in a total quantity of the QoEexperience indicators of the plurality of lower-hierarchy devices is afirst similarity aggregation degree of the QoE experience indicators ofthe plurality of lower-hierarchy devices of the questionable device.

The second cluster includes the largest quantity of QoE experienceindicators, the proportion of the quantity of QoE experience indicatorsof the second cluster in the total quantity of the QoE experienceindicators of the lower-hierarchy devices is, namely, 75%. Therefore,the first similarity aggregation degree is 75%.

Step 2018: When the first similarity aggregation degree is greater thana first similarity aggregation degree threshold, determine that thequestionable device is a faulty device.

If the first similarity aggregation degree threshold is 70%, the firstsimilarity aggregation degree is greater than the first similarityaggregation degree threshold, and it is determined that the questionabledevice is a faulty device.

According to this implementation, the similarity aggregation degree ofthe QoE experience indicators of the plurality of lower-hierarchydevices of the questionable device is determined, to further determinewhether the questionable device is a faulty device, so that accuracy offault localization is further improved.

Optionally, according to the foregoing process of determining thequestionable unit, after the questionable device is determined, the QoEexperience indicators of the device internal units of the questionabledevice may be analyzed to determine the questionable unit; or after thequestionable device is determined as the faulty device, the QoEexperience indicators of the device internal units of the faulty devicemay be analyzed to determine the questionable unit. The manner in whichthe questionable unit is determined may be any manner that is includedin FIG. 5A to 5D and in which the QoE experience indicators of thedevice internal units of the faulty device are analyzed, to determinethe questionable unit in the device internal units.

In one example, after the questionable unit is determined, a similarityaggregation degree of a plurality of lower-hierarchy units of thequestionable unit may further be analyzed to determine whether thequestionable unit is a faulty unit. As shown in FIG. 6B, in addition tosteps 201 to 204 and step 209, the method further includes the followingsteps.

Step 2019: Cluster the QoE experience indicators of a plurality oflower-hierarchy units of the questionable unit, where each clusterincludes at least one QoE experience indicator.

Step 2020: Determine that a proportion of a cluster including a largestquantity of QoE experience indicators in a total quantity of the QoEexperience indicators of the plurality of lower-hierarchy units is asecond similarity aggregation degree of the QoE experience indicators ofthe plurality of lower-hierarchy units of the questionable unit.

Step 2021: When the second similarity aggregation degree is greater thana second similarity aggregation degree threshold, determine that thequestionable unit is a faulty unit.

The manner in which the questionable unit is determined as the faultyunit is similar to the manner in which the questionable device isdetermined as the faulty device. Details are not described herein again.

In one example, after the questionable unit is determined, a similarityaggregation degree of a plurality of lower-hierarchy units of aplurality of questionable units may further be analyzed to determinewhether the questionable units are faulty units. As shown in FIG. 6C, inaddition to steps 201 to 204 and step 209, the method further includesthe following steps.

Step 2022: When there is a plurality of same hierarchy questionableunits, determine a third similarity aggregation degree of the QoEexperience indicators of the plurality of lower-hierarchy units of theplurality of questionable units.

Step 2023: Determine that the plurality of questionable units are allfaulty units when the third similarity aggregation degree is greaterthan a third similarity aggregation degree threshold, and a proportionof a quantity of QoE experience indicators of each lower-hierarchy unitin a cluster corresponding to the third similarity aggregation degree ina total quantity of the QoE experience indicators of a questionable unitto which the lower-hierarchy unit belongs is greater than a presetproportion.

According to this implementation, when a questionable unit is determinedas a faulty unit, a joint analysis may be performed on QoE experienceindicators of lower-hierarchy units of a plurality of questionableunits. For example, a questionable unit 1 has a lower-hierarchy unit 11,a lower-hierarchy unit 12, and a lower-hierarchy unit 13, and aquestionable unit 2 has a lower-hierarchy unit 21, a lower-hierarchyunit 22, and a lower-hierarchy unit 23. A similarity aggregation degreeof the QoE experience indicator of the lower-hierarchy unit 11, the QoEexperience indicator of the lower-hierarchy unit 12, the QoE experienceindicator of the lower-hierarchy unit 13, the QoE experience indicatorof the lower-hierarchy unit 21, the QoE experience indicator of thelower-hierarchy unit 22, and the QoE experience indicator of thelower-hierarchy unit 23 may be analyzed. If the similarity aggregationdegree is greater than a similarity aggregation degree threshold, and acluster corresponding to the similarity aggregation degree includes theQoE experience indicator of the lower-hierarchy unit 11, the QoEexperience indicator of the lower-hierarchy unit 12, the QoE experienceindicator of the lower-hierarchy unit 21, the QoE experience indicatorof the lower-hierarchy unit 22, and the QoE experience indicator of thelower-hierarchy unit 23. The lower-hierarchy unit 11 and thelower-hierarchy unit 12 belong to the questionable unit 1, and aproportion of a quantity of the QoE experience indicators of thelower-hierarchy unit 11 and the lower-hierarchy unit 12 in a totalquantity of the QoE experience indicators of the questionable unit 1 is.The lower-hierarchy unit 21, the lower-hierarchy unit 22 and thelower-hierarchy unit 23 belong to the questionable unit 2, and aproportion of a quantity of the QoE experience indicators of thelower-hierarchy unit 21, the lower-hierarchy unit 22, and thelower-hierarchy unit 23 in a total quantity of the QoE experienceindicators of the questionable unit 2 is 100%. If a preset proportion is50%, both and 100% are greater than 50%, it is determined that both thequestionable unit 1 and the questionable unit 2 are faulty units.

It may be understood by a person skilled in the art that the foregoingsteps may be combined to form a plurality of possible embodiments. Forexample, one scheme includes steps 201 to 204, and steps 2016 to 2018,that is, the scheme shown in FIG. 6A; another scheme includes steps 201to 204, and steps 209 to 2011, and the scheme is not shown; anotherscheme includes steps 201 to 204, step 209, and steps 2019 to 2021, thatis, the scheme shown in FIG. 6B; and still another scheme includes steps201 to 204, and steps 209, 2022, and 2023, that is, the scheme shown inFIG. 6C.

It should be noted that the first and second in the embodiments of thepresent disclosure are merely used for differentiation, for example, thefirst similarity aggregation degree threshold and the second similarityaggregation degree threshold may be the same, or may be different.

In this embodiment of the present disclosure, the user experience data,the network topology data, and the resource management data that are ofthe video service are obtained, so that the QoE experience indicator ofthe network device can be determined. When the value relationshipbetween the QoE experience indicator of the network device and thedevice screening threshold meets a preset condition, the network deviceis determined as a possible questionable device. Compared with a methodfor locating a fault by monitoring a QoS indicator, this method canbetter reflect user experience and has higher accuracy. Optionally, aquestionable device may further be determined with reference to adistribution characteristic of same hierarchy network devices of thepossible questionable device, and/or a similarity aggregation degreeanalysis is performed on QoE experience indicators of downstream samehierarchy network devices of the possible questionable device or thequestionable device, to further determine whether the possiblequestionable device or the questionable device is faulty, therebyfurther improving accuracy of fault localization. Optionally, in thisembodiment of the present disclosure, only a faulty network device maybe located, and a faulty device internal unit in the network device maybe further located. Therefore, accuracy of fault localization is high.

A processing procedure of the fault localization method provided in theembodiments of the present disclosure is specifically described below.

Step (1): Obtain user experience data, network topology data, andresource management data, configure a QoE experience indicator, anddefine an indicator algorithm of the QoE experience indicator.

The user experience data may include at least one of the followingitems: a vMOS, stalling duration, a stalling proportion, stallingfrequency, an artifact duration proportion, artifact times, an artifactarea proportion, video quality switch times, and a poor qualityproportion of video quality. The network topology data is used torepresent a connection relationship between network devices, including atopology connection relationship or a service path of an existingnetwork, and the service path is used to represent a connectionrelationship between the network devices through which service trafficflows. The resource management data is used to represent a connectionrelationship between user equipment and the network devices (forexample, an OLT). Optionally, the resource management data furtherincludes a connection relationship between the user equipment and eachport of the network device.

In one example, an administrator configures the QoE experience indicatoras required, and defines the indicator algorithm of the QoE experienceindicator. For example, the QoE experience indicator is configured as apoor-QoE rate, a definition of a poor-QoE rate algorithm is used as anexample, and a threshold corresponding to each item of data in the userexperience data may be used to determine whether a user is a poor-QoEuser, thereby collecting statistics on a quantity of poor-QoE users,where the poor-QoE rate is equal to a total quantity of poor-QoE usersdivided by a total quantity of users. The QoE experience indicator maybe but is not limited to the poor-QoE rate. In this embodiment of thepresent disclosure, the poor-QoE rate is used as an example fordescription.

In another example, a correspondence among an item included in the userexperience data, a QoE experience indicator, and an indicator algorithmof the QoE experience indicator is preset, and the QoE experienceindicator and the indicator algorithm of the QoE experience indicatorare determined based on an item included in the obtained user experiencedata. For example, the foregoing correspondence may be shown in but isnot limited to Table 1.

TABLE 1 User QoE Indicator algorithm experience experience of the QoEdata indicator experience indicator vMOS vMOS average vMOS average valuevalue is equal to a sum of vMOSs of users divided by a quantity of usersStalling duration, a Poor-QoE Poor-QoE rate is equal stallingproportion, rate to a total and stalling frequency quantity of poor-QoEusers divided by a total quantity of users

Table 1 is merely an example, and an actual table may include morecorrespondences. It may be learned from Table 1 that, when the userexperience data includes only the vMOS, it may be determined that theQoE experience indicator is the vMOS average value, and a correspondingindicator algorithm of the QoE experience indicator is: the vMOS averagevalue is equal to a sum of vMOSs of users divided by a quantity ofusers; when the user experience data includes the stalling duration, thestalling proportion, and the stalling frequency, it may be determinedthat the QoE experience indicator is the poor-QoE rate, and acorresponding indicator algorithm of the QoE experience indicator is:the poor-QoE rate is equal to a total quantity of poor-QoE users dividedby a total quantity of users.

Step (2): Refer to a schematic diagram of each device-board-portconnection relationship shown in FIG. 7, calculate, based on the userexperience data, a poor-QoE rate in each hierarchy based on adevice-board-subcard-port-link level and based on the topologyconnection relationship or the service path of the existing network, andreflect an overall user experience level in each hierarchy by using thepoor-QoE rate. The poor-QoE rate is equal to a quantity of alldownstream poor-QoE users connected to a device internal unit or adevice divided by a quantity of all downstream users connected to thedevice internal unit or the device. FIG. 8 is a schematic diagram ofcalculating an OLT poor-QoE rate. Referring to FIG. 8, a total of tenuser equipments are located below the OLT, that is, service paths of atotal of ten downstream user equipments of the OLT go through the OLT.The first step is to collect statistics to determine whether a userserved by the user equipment is a poor-QoE user, 1 is used to representa poor-QoE user, and 0 is used to represent a non-poor-QoE user. Astatistics result shows that there are four poor-QoE users. The secondstep is to calculate a poor-QoE rate and the poor-QoE rate is 40%.

Step (3): Determine a threshold through threshold learning such as apoor-QoE rate of devices of a same type, that is, determine a devicescreening threshold, and sift out a possible questionable device basedon the device screening threshold for further localization analysis.Optionally, network devices of each type have a device screeningthreshold. For example, an ONT has a device screening threshold and theOLT has another device screening threshold. FIG. 9 is a schematicdiagram of sifting a possible questionable OLT by using a poor-QoE ratethreshold. Referring to FIG. 9, OLT devices are used as an example.Poor-QoE rates of four OLTs that are calculated according to theforegoing poor-QoE rate formula are shown in a table in FIG. 9. It isassumed that the poor-QoE rate threshold is 1%, that is, the devicescreening threshold is 1%. The poor-QoE rate of an OLT 4 exceeds thepoor-QoE rate threshold, and the OLT 4 is initially determined as thepossible questionable OLT. An equilibrium skew analysis and a similarityanalysis are further needed to be performed on the OLT 4. The poor-QoErates of the remaining OLTs connected to a same BRAS are all relativelylow, a problem of the upstream BRAS is excluded, and a fault is locatedto the OLT 4. It may be understood that the OLT 4 is the possiblequestionable device, and in view of accuracy of fault localization,possibilities that a device internal unit of the possible questionabledevice and a downstream device of the OLT 4 are faulty are not excluded.

Step (4): For the sifted possible questionable device, performstatistics and analysis on a distribution characteristic of poor-QoErates, quantities of online users, and the like of same hierarchynetwork devices including the possible questionable device, anddetermine a questionable device. Optionally, statistics and analysis mayfurther be performed on a distribution characteristic of poor-QoE ratesof same hierarchy network devices in topology upstream of the possiblequestionable device; or statistics and analysis may be performed on adistribution characteristic of poor-QoE rates of network devices intopology downstream of the possible questionable device; or statisticsand analysis may be performed on a distribution characteristic ofpoor-QoE rates of device internal units in each hierarchy of thequestionable device; or statistics and analysis may be performed on adistribution characteristic of quantities of online users of samehierarchy network devices of the questionable device. A questionabledevice or a questionable unit is initially located based on anequilibrium skew distribution pattern in a hierarchy. An equilibriumskew analysis may include the following processes.

Step a: Collect statistics on values of data such as the poor-QoE rateand the quantity of online users, where the values of data such as thepoor-QoE rate and the quantity of online users may be an average valuewithin a period of time. The average value may be calculated as a meanvalue, a weighted average value, or the like. In this example, theweighted average value within a one-hour period is calculated asfollows:

${{{One}\text{-}{hour}\mspace{14mu}{poor}\text{-}{QoE}\mspace{14mu}{rate}} = {\sum\limits_{i = 1}^{12}\left( {W_{i}*{X_{i}/Y}} \right)_{i}}},{and}$${W_{i} = {X_{i}/{\sum\limits_{i = 1}^{12}X_{i}}}},$

where i represents i^(th) sampling within one hour, it is assumed thatsampling is performed 12 times in the foregoing formula, and i=1represents the first sampling; Y_(i) represents a quantity of onlineusers during the i^(th) sampling; X_(i) represents a quantity ofpoor-QoE users during the i^(th) sampling; and W_(i) represents a weightduring the i^(th) sampling. It may be understood that the statisticalpoor-QoE rate in step a is a time average value of poor-QoE rates of asame network device or device internal unit within a period of time, andto distinguish from later-mentioned average values of poor-QoE rates ofa plurality of network devices, the time average value of thestatistical poor-QoE rate in step a is referred to as a poor-QoE ratefor short.

Step b: Analyze a distribution pattern of the poor-QoE rates of samehierarchy network devices, configure corresponding thresholds (such as acoefficient of variation threshold and a poor-QoE rate threshold) basedon a requirement, collect statistics on related a distributioncharacteristic of the poor-QoE rates, such as a coefficient of variation(cv) of the poor-QoE rates and an average value of the poor-QoE rates,and locate a possible questionable device. The coefficient of variationis equal to a standard deviation divided by an average value. That is, acoefficient of variation of the poor-QoE rates of same hierarchy networkdevices is equal to a standard deviation of the poor-QoE rates of thesame hierarchy network devices divided by an average value of thepoor-QoE rates of the same hierarchy network devices.

If the coefficient of variation is greater than the coefficient ofvariation threshold (for example, 0.4), it is considered that thepoor-QoE rates of the same hierarchy network devices are in skeweddistribution; a skewed device is found, where the skewed device is anetwork device whose poor-QoE rate is an outlier and that is in theplurality of same hierarchy network devices (for example, when thepoor-QoE rate is greater than an average value of poor-QoE rates, thepoor-QoE rate is referred to as the outlier), and the skewed device is aquestionable device.

If the coefficient of variation is less than or equal to the coefficientof variation threshold, it is considered that the poor-QoE rates of thesame hierarchy network devices are in even distribution (non-skeweddistribution); if the average value of the poor-QoE rates is relativelyhigh (for example, the average value of the poor-QoE rates is greaterthan the poor-QoE rate threshold of the network device), it isdetermined that there is a possible questionable device in upstreamnetwork devices.

Optionally, a distribution pattern of poor-QoE rates of same hierarchydevice internal units may be analyzed in a same manner as that foranalyzing the distribution pattern of the poor-QoE rates of the networkdevices, to determine the questionable unit.

FIG. 10A is a schematic diagram showing distribution of a poor-QoE rateof each PON board of the possible questionable OLT 4 in FIG. 9. Aftercalculation, the coefficient of variation of the poor-QoE rate exceedsthe coefficient of variation threshold (for example, 0.4), and it isconfirmed that the poor-QoE rates of the PON boards of the possiblequestionable OLT 4 are in skewed distribution. In addition, the poor-QoErate (87%) of a PON board 2 (GPON 0/2) far exceeds the average value(12.1%) of the poor-QoE rates, and the PON board 2 (GPON 0/2) is askewed PON board. In a next step, a quantity of online users of the PONboard 2 is further analyzed.

Step c: Analyze a quantity of online users of the questionable device,and compare the quantity of online users of the questionable device witha quantity of online users of all the same hierarchy network devices,for example, calculate a lower confidence limit of distribution of thequantity of online users of all the same hierarchy network devices, tofurther locate the questionable device. The lower confidence limit iscalculated as follows:

${{{Lower}\mspace{14mu}{confidence}\mspace{14mu}{limit}} = {{mean} - {{confidence}*\sqrt{\frac{1}{n}*{\sum\limits_{i = 1}^{n}\left( {x_{i} - {mean}} \right)^{2}}}}}},$where mean is an average value of the quantity of online users of thesame hierarchy network devices, and confidence is a key valuecorresponding to a confidence interval (that is, a corresponding keyvalue when it is verified that statistical quantities are in standardnormal distribution). For example, in this example, a confidence valuecorresponding to the confidence interval of 80% is 1.28.

If the quantity of online users of the network device is not less thanthe lower confidence limit, the quantity of online users is consideredto be at a normal level.

If the quantity of online users of the network device is less than thelower confidence limit, the quantity of online users is excessivelysmall, and the questionable device is excluded. That is, thequestionable device located in step b may be reconfirmed as a normaldevice based on the quantity of online users in step c.

Optionally, a quantity of online users of the questionable unit mayfurther be analyzed, the quantity of online users of the questionableunit is compared with a quantity of online users of all the samehierarchy device internal units, for example, a lower confidence limitof distribution of the quantity of online users of all the samehierarchy device internal units is calculated, to further locate thequestionable unit. The lower confidence limit is calculated as follows:

${{{Lower}\mspace{14mu}{confidence}\mspace{14mu}{limit}} = {{mean} - {{confidence}*\sqrt{\frac{1}{n}*{\sum\limits_{i = 1}^{n}\left( {x_{i} - {mean}} \right)^{2}}}}}},$where mean is an average value of the quantity of online users of thesame hierarchy device internal units, and confidence is a key valuecorresponding to a confidence interval (that is, a corresponding keyvalue when it is verified that statistical quantities are in standardnormal distribution). For example, in the example, a confidence valuecorresponding to the confidence interval of 80% is 1.28.

If the quantity of online users of the device internal unit is not lessthan the lower confidence limit, the quantity of online users isconsidered to be at a normal level.

If the quantity of online users of the device internal unit is less thanthe lower confidence limit, the quantity of online users is excessivelysmall, and the questionable unit is excluded. That is, the questionableunit located in step b may be reconfirmed as a normal unit based on thequantity of online users in step c.

FIG. 10B is a schematic diagram showing distribution of quantities ofonline users of the PON boards of the possible questionable OLT 4 inFIG. 9. In the previous step, it is found that the PON board 2 is askewed PON board, namely, a questionable unit, and the quantity (33) ofonline users of the PON board 2 is further analyzed. The quantity isgreater than a lower confidence limit of a quantity of online users of asame hierarchy PON board (a lower confidence limit corresponding to 80%confidence is 21.71), and it is confirmed that the quantity of onlineusers of the PON board 2 is normal, and the PON board 2 is the skewedPON board, namely, the questionable unit.

Step d: Analyze a distribution pattern of the poor-QoE rates of thelower-hierarchy devices of the questionable device confirmed in step c,and if the poor-QoE rates are evenly distributed and relatively high,further confirm the questionable device, to further analyze similarityof poor quality behaviors of the lower-hierarchy devices of thequestionable device; or analyze a distribution pattern of the poor-QoErates of the lower-hierarchy units of the questionable unit confirmed instep c, and if the poor-QoE rates are evenly distributed and relativelyhigh, further confirm the questionable unit, to further analyzesimilarity of poor quality behaviors of the lower-hierarchy units of thequestionable unit.

In this embodiment of the present disclosure, the skewed device may beunderstood as a questionable device, and the skewed unit may beunderstood as a questionable unit.

FIG. 11A illustrates that the PON board 2 in FIG. 10A is a skewed PONboard; FIG. 11B is a diagram showing distribution of a poor-QoE rate ofeach PON port of the PON board 2; FIG. 11C shows an example of a poorquality behavior correlation matrix of the PON ports of the PON board 2(a darker color indicates a larger value and higher similarity).

As shown in FIG. 11B, in an example, the poor-QoE rate of each PON portof the PON board 2 is further analyzed, a coefficient of variation ofthe poor-QoE rates is calculated to be 0.07, which does not exceed thecoefficient of variation threshold (for example, 0.4), and it isconfirmed that the poor-QoE rates of the PON ports are evenlydistributed. An average value (92.7%) of overall poor-QoE rates of thePON ports far exceeds a poor-QoE rate threshold (10%), and it isdetermined that the poor-QoE rates of the PON ports are evenlydistributed and relatively high, and the PON board 2 is a skewed PONboard. The similarity of the poor quality behaviors of the PON portsneeds to be further analyzed. If the poor quality behaviors of the PONports are similar, it may be determined that the PON board 2 isquestionable, resulting in that all the PON ports are questionable, andthe PON board 2 is a faulty PON board. That is, if the poor qualitybehaviors of the lower-hierarchy units of the questionable unit aresimilar, it may be further determined that the questionable unit is afaulty unit.

Step (5): Perform a similarity aggregation degree analysis on subnodesof a skewed device (a lower-hierarchy device or a lower-hierarchy userequipment) to further confirm the faulty device. Optionally, asimilarity aggregation degree analysis may be further performed onsubnodes of a skewed unit (a lower-hierarchy unit or a lower-hierarchyuser equipment) to further confirm the faulty unit. Similaritylocalization analysis enables two functions: (i) A similarityaggregation degree analysis is performed on sub-nodes of a single skeweddevice or skewed unit by using algorithms such as data mining, tofurther confirm a faulty device or a faulty unit; and if a similarityaggregation degree is relatively high (for example, greater than aspecific threshold), it indicates that poor quality behaviors of thesub-nodes connected to the single skewed device or skewed unit are allbasically similar, and it is confirmed that an identified questionabledevice is a faulty device or that an identified questionable unit is afaulty unit; (ii) A similarity aggregation degree analysis is performedon sub-nodes of a plurality of skewed devices or skewed units, toanalyze proportions of similar poor quality behaviors, and if theproportion of similar sub-nodes in each skewed device or skewed unit isrelatively high (for example, greater than 50%) in a cluster with arelatively high similarity aggregation degree (for example, greater thana specific threshold), some poor-QoE units or users due to a local faultof a network element may be located, such as a cross-board fault and across-board local port fault. A specific analysis may be combined with acorrelation coefficient, a data mining clustering algorithm (forexample, a density-based spatial clustering of applications with noise(DBSCAN) algorithm), and the like, to calculate a similarity aggregationdegree of objects of a skewed device or skewed unit. The analysis inthis example includes the following steps.

Step a: Calculate a correlation coefficient of poor-QoE rates betweenobjects to obtain a correlation matrix. A correlation coefficientcalculation formula is:

${{\rho\left( {x,y} \right)} = \frac{{cov}\left( {x,y} \right)}{\sqrt{{{var}(x)}{{var}(y)}}}},$where cov(x,y) is covariance, and var(x) and var(y) are respectivelyvariance of x and variance of y.

Step b: Cluster the correlation matrix based on that the correlationcoefficient is greater than or equal to a specific value (for example,0.3: moderate or high correlation) and in combination with data miningclustering algorithms such as DBSCAN algorithms, to obtain each clusterincluding similar objects.

Step c: Calculate a proportion of each cluster, where the proportion ofeach cluster is equal to a total quantity of objects in the clusterdivided by a total quantity of objects.

For example, the total quantity of objects is 10, and two clusters areobtained by using the clustering algorithm in step b. If the totalquantity of objects in a cluster is 6, the proportion of the cluster is60%; a total quantity of objects in the other cluster is 4, and theproportion of the other cluster is 40%. A largest proportion in eachcluster is determined as the similarity aggregation degree.

Step d: Determine whether a similarity aggregation degree is greaterthan a specific threshold, for example, 80%.

FIG. 11C shows a poor quality behavior correlation matrix of the PONports of the PON board 2, and the poor quality behaviors of the PONports are in pairwise correlation. In combination with an algorithm suchas clustering, the similarity aggregation degree is calculated to be100%, which exceeds the similarity aggregation degree threshold (80%),and the PON board 2 is confirmed to be questionable, and the fault isfinally located to the PON board 2.

Step (6): Determine whether there is an exception by combining a resultof an equilibrium skew localization module and a similarity localizationmodule in real time, and if there is an exception, such as a deviceexception, or a locally serviceable unit such as aboard-subcard-port-link-level exception of a device, give an alarm. Inthis example, it is confirmed that the PON board 2 is abnormal, and analarm is given.

In this embodiment of the present disclosure, by using the userexperience data, the QoE experience indicators are calculated layer bylayer by using a topology dimension. This can reflect an overall userexperience level and improve accuracy of fault localization. Accordingto a plurality of intelligent methods such as the distributioncharacteristic and the similarity aggregation degree, a fault isautomatically located without manual configuration of the thresholds,and accuracy is high. Experience distribution after aggregation may bemonitored in real time to locate a fault. The intelligent methods may beused for device-level fault localization, and may also be used fordevice-local board-subcard-port-link-level fault localization.

FIG. 12 is a schematic structural diagram of a fault localization deviceaccording to an embodiment of the present disclosure. The device isconfigured to perform the intelligent group barrier localization methodprovided in the embodiments of the present disclosure. For correspondingfeatures and descriptions, refer to content related to the foregoingmethod. Details are not described in this embodiment again. The deviceincludes: a data monitoring obtaining module 1201 configured to monitoruser experience data, network topology data, and resource managementdata that are of IPTV; a data aggregation generation module 1202configured to determine, based on the user experience data, the networktopology data, and the resource management data monitored by the datamonitoring obtaining module 1201, a QoE experience indicator in eachhierarchy according to a device-board-subcard-port-link hierarchy andbased on the QoE experience indicator and an indicator algorithmcorresponding to the QoE experience indicator; a possible questionabledevice monitoring screening module 1203 configured to sift out at leastone possible questionable device based on the QoE experience indicatorin a device hierarchy that is determined by the data aggregationgeneration module 1202 and a device screening threshold; and anequilibrium skew localization module 1204 configured to: determine afirst distribution characteristic value of the QoE experience indicatorsthat are in the device hierarchy and that are of same hierarchy devicesof each possible questionable device sifted out by the possiblequestionable device monitoring screening module 1203, and initiallydetermine, based on a value relationship between the first distributioncharacteristic value and an equilibrium skew threshold, that thepossible questionable device is a questionable device or that anupstream device of the possible questionable device is a questionabledevice; and determine a second distribution characteristic value of theQoE experience indicators of device internal units in each hierarchy ofeach questionable device, and locate at least one questionable deviceinternal unit of each questionable device based on a value relationshipbetween the second distribution characteristic value and the equilibriumskew threshold.

Optionally, the device further includes: a similarity localizationmodule 1205 configured to: after the equilibrium skew localizationmodule 1204 locates the questionable device or the questionable deviceinternal unit, analyze a first similarity aggregation degree of aplurality of lower-hierarchy devices of each questionable device and asecond similarity aggregation degree of a plurality of lower-hierarchyunits of each questionable device internal unit, to ultimately determinewhether the questionable device and the questionable device internalunit are faulty; and/or analyze a third similarity aggregation degree ofa plurality of lower-hierarchy units of a plurality of questionabledevice internal units to ultimately determine whether the plurality ofquestionable device internal units are faulty.

It may be understood that after the fault localization is completed, anexception alarm of the device or device internal unit may further begiven.

In one example, the equilibrium skew localization module 1204 is furtherconfigured to: determine the first distribution characteristic value ofthe QoE experience indicators that are in the device hierarchy and thatare of same hierarchy devices of each possible questionable device, andwhen the first distribution characteristic value is greater than theequilibrium skew threshold, initially determine that a skew device inthe same hierarchy devices of the possible questionable device is aquestionable device; and when the first distribution characteristicvalue is less than or equal to the equilibrium skew threshold, initiallydetermine that there is no questionable device in the same hierarchydevices of the possible questionable device; and determine the seconddistribution characteristic value of the QoE experience indicators ofthe device internal units in each hierarchy of each questionable device,and when the second distribution characteristic value is greater thanthe equilibrium skew threshold, initially determine that a skew deviceinternal unit of the device internal units in each hierarchy of thequestionable device is a questionable device internal unit; and when thesecond distribution characteristic value is less than or equal to theequilibrium skew threshold, initially determine that there is noquestionable device internal unit in the device internal units in eachhierarchy of the questionable device.

In one example, the similarity localization module 1205 is furtherconfigured to: determine a first similarity aggregation degree of aplurality of lower-hierarchy devices of each questionable device, andultimately determine that the questionable device is faulty if aproportion of a cluster whose first similarity aggregation degree isgreater than a similarity aggregation degree threshold is greater than apreset proportion; and determine a second similarity aggregation degreeof a plurality of lower-hierarchy units of each questionable deviceinternal unit, and ultimately determine that the questionable deviceinternal unit is faulty if a proportion of a cluster whose secondsimilarity aggregation degree is greater than the similarity aggregationdegree threshold is greater than a preset proportion; and/or determine athird similarity aggregation degree of a plurality of lower-hierarchyunits of a plurality of questionable device internal units, andultimately determine that the plurality of questionable device internalunits are faulty if a proportion of a cluster whose third similarityaggregation degree is greater than the similarity aggregation degreethreshold is greater than a preset proportion.

In one example, the user experience data includes at least one of thefollowing items: a VMOS, stalling duration, a stalling proportion,stalling frequency, an artifact duration proportion, artifact times, anartifact area proportion, video quality switch times, and a poor qualityproportion of video quality; the network topology data includes atopology connection relationship or a service path of an existingnetwork; the resource management data includes a connection relationshipbetween user equipment and each network device and a connectionrelationship between the user equipment and each port of the networkdevices; and the QoE experience indicator is a poor-QoE rate.

An indicator algorithm corresponding to the poor-QoE rate is: thepoor-QoE rate is equal to a total quantity of poor-QoE users divided bya total quantity of users. Whether a user corresponding to the userexperience data is a poor-QoE user is determined based on a valuerelationship between the user experience data and an experiencethreshold.

In one example, the data monitoring obtaining module 1201, the possiblequestionable device monitoring screening module 1203, the equilibriumskew localization module 1204, and the similarity localization module1205 may all provide input interfaces for configuring information suchas an algorithm and a parameter.

The data monitoring obtaining module 1201 provides an input interface,including collection of a user experience indicator, a network topology,resource management data, and a configured QoE experience indicator (oneor more indicators that are used to represent user experience, such asthe vMOS and the stalling duration proportion) and a correspondingindicator algorithm, such as a poor-QoE rate, and stores configurationinformation into a database or a configuration file.

The data aggregation generation module 1202 is configured to: aggregatethe poor-QoE rates layer by layer based on the experience data accordingto a topology or the service path, and according to adevice-board-subcard-port-link level, and calculate an overall userexperience level in each hierarchy.

The possible questionable device monitoring screening module 1203 isconfigured to sift out a possible questionable device through thresholdlearning such as a poor-QoE rate of devices of a same type, so as tofurther perform a localization analysis on the possible questionabledevice.

The equilibrium skew localization module 1204 is configured to: performstatistics and analysis on a distribution characteristic of the poor-QoErates, quantities of online users, and the like, where the poor-QoErates, the quantities of online users, and the like are those oftopology upstream and downstream of the sifted possible questionabledevice and those of the device internal units in each hierarchy, andinitially locate the questionable device or the questionable unit basedon an equilibrium skewed distribution pattern in a hierarchy.

The similarity localization module 1205 is configured to perform asimilarity aggregation degree analysis on sub-nodes (lower-hierarchydevices or units or users) of a possible faulty network element or alocal faulty unit that are located by using equilibrium skew, to furtherconfirm a fault, and locate some poor-QoE units or users due to a localfault of a network element. The similarity localization module enablestwo functions: (i) A similarity aggregation degree analysis is performedon sub-nodes of a single skewed device or skewed unit by usingalgorithms such as data mining, to further confirm a fault; and if thesimilarity aggregation degree is relatively high (for example, greaterthan a specific threshold), it indicates that poor quality behaviors ofthe sub-nodes connected to the single skewed device or skewed unit arebasically similar, and it is confirmed that the questionable deviceidentified by the equilibrium skew localization module is a faultydevice or that the identified questionable unit is a faulty unit; (ii) Asimilarity aggregation degree analysis is performed on sub-nodes of aplurality of skewed devices or skewed units to analyze a proportion ofsimilar poor quality behaviors, and if the proportion of similarsub-nodes in each skewed device or skewed unit is relatively high (forexample, greater than 50%) in a cluster with a relatively highsimilarity aggregation degree (for example, greater than a specificthreshold), some poor-QoE units or users due to a local fault of anetwork element may be located, such as a cross-board fault and across-board local port fault.

Device or device internal unit exception alarm: A poor qualitylocalization system determines whether there is an exception bycombining a result of the equilibrium skew localization module and thesimilarity localization module in real time, and if there is anexception, such as a device exception, or a locally serviceable unitsuch as a board-subcard-port-link-level exception of the device, givesan alarm.

Based on the device structure shown in FIG. 12, FIG. 13 is a schematicdiagram of interaction of each module according to an embodiment of thepresent disclosure. Refer to FIG. 13, configuration may be performed onoperations labeled with *, and operations labeled with sequence numbersare completed by modules. The operations completed by the modules aredescribed as follows:

Step 1301: Configure a QoE experience indicator and a correspondingindicator algorithm, such as a poor-QoE rate, based on monitored userexperience data, network topology data, and resource management data.

In this embodiment of the present disclosure, the QoE experienceindicator and the corresponding indicator algorithm may be configuredfirst, and then user experience data, network topology data, andresource management data are monitored; or user experience data, networktopology data, and resource management data may be monitored first, andthen the QoE experience indicator and the corresponding indicatoralgorithm are configured based on the monitored user experience data,network topology data, and resource management data.

Step 1302: Based on the user experience data, calculate the poor-QoErate layer by layer according to a topology path and a device-board-portlevel, and determine an overall user experience level in each hierarchy.

Step 1303: Configure a device screening threshold (such as a poor-QoErate threshold), and sift out a possible questionable device throughthreshold learning such as a poor-QoE rate of devices of a same type,for further localization analysis.

Step 1304: Configure an equilibrium skew threshold (such as acoefficient of variation threshold and a poor-QoE rate threshold), andanalyze a distribution characteristic of poor-QoE rates and the like,where the poor-QoE rates and the like are those of topology upstream anddownstream of the possible questionable device and those of deviceinternal units in each hierarchy, to locate a questionable device or aquestionable unit.

Step 1305: Configure a similarity localization module threshold (such asa parameter and a threshold that are related to a similarity aggregationdegree algorithm), and further locate some poor-QoE units or users dueto a local fault of a network element by analyzing a similarityaggregation degree.

It may be understood that, to implement the foregoing functions, thefault localization device includes a corresponding hardware structureand/or software module for implementing each function. A person skilledin the art should be easily aware that, the units and algorithm steps inthe examples described with reference to the embodiments disclosed inthis specification may be implemented by hardware or a combination ofhardware and computer software. Whether a function is implemented byhardware or in a manner of driving hardware by a computer softwaredepends on a particular application and a design constraint condition ofthe technical solution. A person skilled in the art may use differentmethods to implement the described functions for each particularapplication, but it should not be considered that the implementationgoes beyond the scope of the present disclosure.

In this embodiment of the present disclosure, functional module divisionmay be performed on the device according to the foregoing methodexamples. For example, each function module can be divided for eachfunction, or two or more functions can be integrated into one processingmodule. The integrated module may be implemented in a form of hardware,or may be implemented in a form of a functional module of software. Itshould be noted that, the module division in the embodiments of thepresent disclosure is an example and is only logical function division.There may be another division manner in actual implementation.

When an integrated module is used, FIG. 14 is a possible schematicstructural diagram of the fault localization device described in theforegoing embodiments. A fault localization device 1400 includes aprocessing module 1402 and a communications module 1403. The processingmodule 1402 is configured to control and manage actions of the device.For example, the processing module 1402 is configured to support thedevice in performing the processes described in FIG. 2, FIG. 3A to FIG.3D, FIG. 4A to FIG. 4C, FIG. 5A to FIG. 5D, and FIG. 6A to FIG. 6C,and/or performing other processes of the technology described in thisspecification. The communications module 1403 is configured to supportcommunication between the device and another network entity, such ascommunication with a network device. The fault localization device mayfurther include a storage module 1401 configured to store program codeand data of the device.

Corresponding to FIG. 12, the processing module 1402 may be configuredto implement functions of one or more of the data monitoring obtainingmodule 1201, the data aggregation generation module 1202, the possiblequestionable device monitoring screening module 1203, the equilibriumskew localization module 1204, and the similarity localization module1205.

The processing module 1402 may be a processor or a controller, such as acentral processing unit (CPU), a general purpose processor, a digitalsignal processor (DSP), an application-specific integrated circuit(ASIC), a field-programmable gate array (FPGA), or another programmablelogic device, a transistor logic device, a hardware component, or anycombination thereof. The processor module 1402 can implement or executevarious example logical blocks, modules, and circuits that are describedwith reference to the content disclosed in the present disclosure. Theprocessor may be a combination for implementing a computing function,for example, a combination of one or more microprocessors or acombination of the DSP and a microprocessor, and the like. Thecommunications module 1403 may be a communications interface, atransceiver, a transceiver circuit, or the like. The communicationsinterface is a collective term and may include one or more interfaces.The storage module 1401 may be a memory.

In this embodiment of the present disclosure, the processing module 1402controls the communications module 1403 to obtain user experience data,network topology data, resource management data that are of a videoservice, so as to determine a QoE experience indicator of a networkdevice. Because the QoE experience indicator of the network device isdetermined based on user experience data of user equipment served by thenetwork device. This is different from a manner in which a QoS indicatorof the network device is directly determined by obtaining a parameter ofthe network device. Compared with a method for performing faultlocalization by monitoring the QoS indicator, this method can betterreflect user experience and has high accuracy.

When the processing module 1402 is a processor, the communicationsmodule 1403 is a communications interface, and the storage module 1401is a memory, the fault localization device used in the embodiments ofthe present disclosure may be a device shown in FIG. 15.

As shown in FIG. 15, the fault localization device 1500 includes aprocessor 1502, a communications interface 1503, and a memory 1501. Thecommunications interface 1503, the processor 1502, and the memory 1501may be connected to each other by using a communication connection.

The methods or algorithm steps described with reference to the contentdisclosed in the present disclosure may be implemented in a hardwaremanner, or may be implemented in a manner of executing a softwareinstruction by a processor. The software instruction may include acorresponding software module. The software module may be stored in arandom-access memory (RAM), a flash memory, a read-only memory (ROM), anerasable programmable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), a register, a hard disk, aremovable hard disk, a compact disc read-only memory (CD-ROM), or astorage medium in any other forms well-known in the art. A storagemedium used as an example is coupled to the processor, so that theprocessor can read information from the storage medium, and can writeinformation into the storage medium. Certainly, the storage medium maybe a component of the processor. The processor and the storage mediummay be located in an ASIC. In addition, the ASIC may be located in acore network interface device. Certainly, the processor and the storagemedium may exist in the core network interface device as discretecomponents.

A person skilled in the art should be aware that in the foregoing one ormore examples, functions described in the present disclosure may beimplemented by hardware, software, firmware, or any combination thereof.When this disclosure is implemented by software, these functions may bestored in a computer-readable medium or transmitted as one or moreinstructions or code in the computer-readable medium. Thecomputer-readable medium includes a computer storage medium and acommunications medium, where the communications medium includes anymedium that enables a computer program to be transmitted from one placeto another. The storage medium may be any available medium accessible toa general-purpose or dedicated computer.

The objectives, technical solutions, and beneficial effects of thepresent disclosure are further described in detail in the foregoingspecific implementations. It should be understood that the foregoingdescriptions are merely specific implementations of the presentdisclosure, but are not intended to limit the protection scope of thepresent disclosure. Any modification, equivalent replacement, orimprovement made based on the technical solutions of the presentdisclosure shall fall within the protection scope of the presentdisclosure.

What is claimed is:
 1. A fault localization method comprising: obtaininguser experience data, network topology data, and resource managementdata of a video service, wherein the network topology data represent afirst connection relationship between network devices, and wherein theresource management data represent a second connection relationshipbetween user equipments (UEs) and the network devices; determining aquality of experience (QoE) experience indicator of a first networkdevice based on the user experience data, the network topology data, andthe resource management data; determining the first network device as apossible questionable device when a QoE represented by the QoEexperience indicator is lower than a device screening threshold;analyzing a first distribution characteristic of first QoE experienceindicators of a plurality of same hierarchy network devices comprisingthe possible questionable device; and determining a second networkdevice whose QoE experience indicator is an outlier and that is in thesame hierarchy network devices as a questionable device when the firstQoE experience indicators are in a first skewed distribution.
 2. Thefault localization method of claim 1, further comprising: determining afirst UE served by the first network device based on the networktopology data and the resource management data; and further determiningthe QoE experience indicator based on first user experience data of thefirst UE.
 3. The fault localization method of claim 1, wherein the userexperience data comprise at least one of a video mean opinion score(vMOS), a stalling duration, a stalling proportion, a stallingfrequency, an artifact duration proportion, artifact times, an artifactarea proportion, video quality switch times, or a poor qualityproportion of video quality.
 4. The fault localization method of claim1, wherein the network topology data comprise a topology connectionrelationship or a service path of an existing network, and wherein theservice path represents a third connection relationship between thenetwork devices through which service traffic flows.
 5. The faultlocalization method of claim 1, further comprising: determining a firstdistribution characteristic value of the first QoE experienceindicators, wherein the first distribution characteristic valuerepresents whether the first QoE experience indicators are in the firstskewed distribution; making a determination that the first QoEexperience indicators are in the first skewed distribution when thefirst distribution characteristic value is greater than a firstequilibrium skew threshold; and further determining, based on thedetermination, the second network device as the questionable device. 6.The fault localization method of claim 1, further comprising:determining a first overall characteristic value of the first QoEexperience indicators, wherein the first overall characteristic valuerepresents an average level of the first QoE experience indicators; andfurther determining the second network device as the questionable devicewhen the first QoE experience indicators are in the first skeweddistribution.
 7. The fault localization method of claim 6, furthercomprising: making a determination that the first QoE experienceindicators are not in a skewed distribution and that the first overallcharacteristic value is greater than an empirical threshold; anddetermining, based on the determination, that an upstream network deviceof the same hierarchy network devices is the questionable device.
 8. Thefault localization method of claim 1, wherein after determining thesecond network device as the questionable device, the fault localizationmethod further comprises: determining a second overall characteristicvalue of the first QoE experience indicators, wherein the second overallcharacteristic value represents an average level of the first QoEexperience indicators; and skipping excluding the questionable devicewhen the first QoE experience indicators are not in the first skeweddistribution and that the second overall characteristic value is greaterthan an empirical threshold.
 9. The fault localization method of claim1, wherein the questionable device comprises a plurality of deviceinternal units in at least one hierarchy, and wherein the faultlocalization method further comprises: analyzing a second distributioncharacteristic of second QoE experience indicators of a plurality ofsame hierarchy device internal units of the questionable device; anddetermining a device internal unit whose QoE experience indicator is theoutlier and that is in the same hierarchy device internal units as aquestionable unit when the second QoE experience indicators are in asecond skewed distribution.
 10. The fault localization method of claim9, further comprising: determining an overall characteristic value ofthe second QoE experience indicators, wherein the overall characteristicvalue represents an average level of the second QoE experienceindicators; and determining that the questionable unit is anupper-hierarchy device internal unit of the same hierarchy deviceinternal units when the second QoE experience indicators are not in thesecond skewed distribution and when the overall characteristic value isgreater than an empirical threshold.
 11. The fault localization methodof claim 9, wherein after determining the device internal unit, thefault localization method further comprises: determining an overallcharacteristic value of third QoE experience indicators of a pluralityof same hierarchy lower-hierarchy device internal units of thequestionable unit; and skipping excluding the questionable unit when thethird QoE experience indicators are not in a third skewed distributionand when the overall characteristic value is greater than an empiricalthreshold.
 12. The fault localization method of claim 9, wherein afterdetermining the device internal unit, the fault localization methodfurther comprises: clustering third QoE experience indicators of aplurality of lower-hierarchy units of the questionable unit intoclusters, wherein each of the clusters comprises at least one of thethird QoE experience indicators; determining that a proportion of alargest quantity of the third QoE experience indicators in one of theclusters to a total quantity of the third QoE experience indicators is asecond similarity aggregation degree of the third QoE experienceindicators; and determining that the questionable unit is a faulty unitwhen the second similarity aggregation degree is greater than asimilarity aggregation degree threshold.
 13. The fault localizationmethod according to claim 9, wherein after determining the deviceinternal unit, the fault localization method further comprises:determining a similarity aggregation degree of third QoE experienceindicators of a plurality of lower-hierarchy units of a plurality ofquestionable units when there is a plurality of same hierarchyquestionable units; and determining that the same hierarchy questionableunits are all faulty units when the similarity aggregation degree isgreater than a similarity aggregation degree threshold and when aproportion of a quantity of third QoE experience indicators of eachlower-hierarchy unit in a cluster corresponding to the similarityaggregation degree to a total quantity of fourth QoE experienceindicators of a questionable unit to which the lower-hierarchy unitbelongs is greater than a preset proportion.
 14. The fault localizationmethod of claim 1, wherein after determining the second network device,the fault localization method further comprises: clustering second QoEexperience indicators of a plurality of lower-hierarchy devices of thequestionable device into clusters, wherein each of the clusterscomprises at least one of the second QoE experience indicators;determining that a proportion of a quantity of the second QoE experienceindicators of a first cluster to a total quantity of the second QoEexperience indicators is a first similarity aggregation degree of thesecond QoE experience indicators, wherein the first cluster comprises alargest quantity of the second QoE experience indicators from among theclusters; and determining that the questionable device is a faultydevice when the first similarity aggregation degree is greater than asimilarity aggregation degree threshold.
 15. The fault localizationmethod of claim 1, wherein the QoE experience indicator is a poor-QoErate, wherein either the poor-QoE rate is equal to a first totalquantity of poor-QoE users of the first network device divided by asecond total quantity of users of the first network device or thepoor-QoE rate is equal to a third total quantity of poor-QoE users of aninternal unit of the first network device divided by a fourth totalquantity of users of the internal unit, wherein whether a usercorresponding to the user experience data is a poor-QoE user is based ona value relationship between the user experience data and an experiencethreshold, and wherein the QoE experience indicator is lower than thedevice screening threshold when the poor-QoE rate is less than thedevice screening threshold.
 16. A fault localization device comprising:a memory configured to store instructions; a processor coupled to thememory and configured to execute the instructions to: obtain userexperience data, network topology data, and resource management data ofa video service, wherein the network topology data represent a firstconnection relationship between network devices, and wherein theresource management data represent a second connection relationshipbetween user equipments (UEs) and the network devices; determine aquality of experience (QoE) experience indicator of a first networkdevice based on the user experience data, the network topology data, andthe resource management data; determine the first network device as apossible questionable device when a QoE represented by the QoEexperience indicator is lower than a device screening threshold; analyzea first distribution characteristic of first QoE experience indicatorsof a plurality of same hierarchy network devices comprising the possiblequestionable device; and determining a second network device whose QoEexperience indicator is an outlier and that is in the same hierarchynetwork devices as a questionable device when the first QoE experienceindicators are in a first skewed distribution.
 17. The faultlocalization device of claim 16, wherein the processor is furtherconfigured to: determine a first UE served by the first network devicebased on the network topology data and the resource management data; andfurther determine the QoE experience indicator based on first userexperience data of the first UE.
 18. The fault localization device ofclaim 16, wherein the user experience data comprise at least one of avideo mean opinion score (vMOS), a stalling duration, a stallingproportion, a stalling frequency, an artifact duration proportion,artifact times, an artifact area proportion, video quality switch times,or a poor quality proportion of video quality.
 19. The faultlocalization device of claim 16, wherein the network topology datacomprise a topology connection relationship or a service path of anexisting network, and wherein the service path represents a thirdconnection relationship between the network devices through whichservice traffic flows.
 20. The fault localization device of whereinafter determining the second network device, the processor is furtherconfigured to: cluster the second QoE experience indicators of aplurality of lower-hierarchy devices of the questionable device intoclusters, wherein each of the clusters comprises at least one of thesecond QoE experience indicators; determining that a proportion of aquantity of the second QoE experience indicators of a first cluster to atotal quantity of the second QoE experience indicators is a firstsimilarity aggregation degree of the second QoE experience indicators,wherein the first cluster comprises a largest quantity of the second QoEexperience indicators from among the clusters; and determining that thequestionable device is a faulty device when the first similarityaggregation degree is greater than a similarity aggregation degreethreshold.
 21. A computer program product comprising computer-executableinstructions that are stored on a non-transitory computer-readablemedium and that, when executed by a processor, cause a faultlocalization device to: obtain user experience data, network topologydata, and resource management data of a video service, wherein thenetwork topology data represent a first connection relationship betweennetwork devices, and wherein the resource management data represent asecond connection relationship between user equipment and the networkdevices; determine a quality of experience (QoE) experience indicator ofa first network device based on the user experience data, the networktopology data, and the resource management data; determine that thefirst network device is a possible questionable device when a QoErepresented by the QoE experience indicator is lower than a devicescreening threshold; analyze a first distribution characteristic offirst QoE experience indicators of a plurality of same hierarchy networkdevices comprising the possible questionable device; and determining asecond network device whose QoE experience indicator is an outlier andthat is in the same hierarchy network devices as a questionable devicewhen the first QoE experience indicators are in a first skeweddistribution.
 22. The computer program product of claim 21, wherein thecomputer-executable instructions, when executed by the processor,further cause the fault localization device to: determine a first UEserved by the first network device based on the network topology dataand the resource management data; and further determine the QoEexperience indicator based on first user experience data of the firstUE.
 23. The computer program product of claim 21, wherein the userexperience data comprise at least one of a video mean opinion score(vMOS), a stalling duration, a stalling proportion, a stallingfrequency, an artifact duration proportion, artifact times, an artifactarea proportion, video quality switch times, or a poor qualityproportion of video quality.